Apparatus and method for reproducing sound, and method for canceling a feedback signal

ABSTRACT

Provided are a method and apparatus for reproducing sound. A sound signal may be generated by receiving sound around the apparatus. An output signal may be generated by performing a process according to functions of the apparatus on a residual signal obtained by canceling a feedback signal from the sound signal.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims the benefit under 35 U.S.C. §119(a) of KoreanPatent Application No. 10-2011-0017294, filed on Feb. 25, 2011, in theKorean Intellectual Property Office, the entire disclosure of which isincorporated herein by reference for all purposes.

BACKGROUND

1. Field

The following description relates to an apparatus and method forreproducing sound, and a method of canceling a feedback signal in anapparatus for reproducing sound.

2. Description of the Related Art

An apparatus for reproducing sound may reproduce sound that may be heardby a user. Examples of such an apparatus not only include an audiodevice for reproducing sound, such as an MP3 player or a stereo player,but also a hearing aid for recognizing, amplifying, and reproducingsurrounding sound, and also a telephone or repeater for reproducingexternal sound.

A feedback signal that is generated when sound reproduced by theapparatus is again input to the apparatus may be very unpleasant to theuser. Also, a maximum gain value of the apparatus may be limited by thefeedback signal.

SUMMARY

In one general aspect, there is provided an apparatus for reproducingsound, the apparatus including a sound signal generator configured togenerate a sound signal by receiving sound from around the apparatus, asignal processor configured to perform a process according to functionsof the apparatus, on a residual signal that is obtained by canceling afeedback signal from the sound signal, a coefficient determinerconfigured to determine a linear prediction coefficient that isadaptively adjusted according to the residual signal, a first linearpredictor configured to output a first prediction signal by linearlypredicting the residual signal according to the determined linearprediction coefficient, a second linear predictor configured to output asecond prediction signal by linearly predicting the sound signal that isprocessed by the signal processor, according to the determined linearprediction coefficient, and a feedback signal canceler configured toestimate a path of the feedback signal according to an algorithm forcanceling a feedback signal using the first and second predictionsignals, and to cancel the feedback signal from the sound signalgenerated by the sound signal generator using the estimated path of thefeedback signal.

The algorithm for canceling the feedback signal may comprise a pseudoaffine projection (PAP) algorithm.

The feedback signal canceler may comprise a feedback signal estimatorconfigured to estimate the path of the feedback signal using the firstand second prediction signals, a feedback signal extractor configured toextract the feedback signal from the sound signal that is processed bythe signal processor, using the estimated path of the feedback signal,and a residual signal generator configured to generate the residualsignal by canceling the extracted feedback signal from the sound signalgenerated by the sound signal generator.

The feedback signal extractor may be an adaptive filter that updates acoefficient according to the path of the feedback signal estimated bythe feedback signal estimator.

At least one of the coefficient determiner, the first linear predictor,and the second linear predictor may comprise a lattice-based linearpredictor.

The first linear predictor may be configured to output the firstprediction signal by considering an a priori error.

The coefficient determiner may be configured to determine the linearprediction coefficient according to a Burg algorithm.

The apparatus may further comprise a convergence speed adjustorconfigured to adaptively adjust a convergence speed of the feedbacksignal extractor according to an autocorrelation of the residual signal.

The convergence speed adjustor may be configured to adjust a convergencespeed using a ratio of a first autocorrelation at a first timedifference to a second autocorrelation at a second time difference.

The convergence speed adjustor may be configured to adjust a convergencespeed using a sigmoid function according to the ratio of the firstautocorrelation at the first time difference to the secondautocorrelation at the second time difference.

In another aspect, there is provided a method of reproducing sound in anapparatus for reproducing sound, the method including generating a soundsignal by receiving sound from around the apparatus, generating anoutput signal by performing a process according to functions of theapparatus, on a residual signal that is obtained by canceling a feedbacksignal from the sound signal, determining a linear predictioncoefficient that is adaptively adjusted according to the residualsignal, generating a first prediction signal by linearly predicting theresidual signal according to the determined linear predictioncoefficient, generating a second prediction signal by linearlypredicting the output signal according to the determined linearprediction coefficient, estimating a path of the feedback signalaccording to an algorithm for canceling a feedback signal using thefirst and second prediction signals, and canceling the feedback signalfrom the sound signal using the estimated path of the feedback signal.

The algorithm for canceling the feedback signal may comprise a pseudoaffine projection (PAP) algorithm.

The canceling of the feedback signal may comprise extracting thefeedback signal from the output signal using the estimated path of thefeedback signal, and generating the residual signal by canceling theextracted feedback signal from the sound signal.

At least one of the determining of the linear prediction coefficient,the generating of the first prediction signal, and the generating of thefirst prediction signal may be performed using a lattice-based linearpredictor.

The generating of the first prediction signal may be performed using alattice-based linear predictor by considering an a priori error.

The determining of the linear prediction coefficient may be performedaccording to a Burg algorithm.

The extracting of the feedback signal may be performed using an adaptivefilter, and the method may further comprise adaptively adjusting aconvergence speed of the adaptive filter according to an autocorrelationof the residual signal.

In another aspect, there is provided a method of canceling a feedbacksignal, the method including determining a linear prediction coefficientthat is adaptively adjusted according to an input signal of an apparatusfor reproducing sound, generating a first prediction signal by linearlypredicting the input signal using the determined linear predictioncoefficient, generating a second prediction signal by linearlypredicting an output signal of the apparatus using the determined linearprediction coefficient, estimating a path of a feedback signal accordingto an algorithm for canceling a feedback signal using the first andsecond prediction signals, and canceling the feedback signal from theinput signal using the estimated path of the feedback signal.

In another aspect, there is provided a computer-readable storage mediumhaving stored therein program instructions to cause a processor toimplement a method of reproducing sound in an apparatus for reproducingsound, the method including generating a sound signal by receiving soundfrom around the apparatus, generating an output signal by performing aprocess according to functions of the apparatus, on a residual signalthat is obtained by canceling a feedback signal from the sound signal,determining a linear prediction coefficient that is adaptively adjustedaccording to the residual signal, generating a first prediction signalby linearly predicting the residual signal according to the determinedlinear prediction coefficient, generating a second prediction signal bylinearly predicting the output signal according to the determined linearprediction coefficient, estimating a path of the feedback signalaccording to an algorithm for canceling a feedback signal using thefirst and second prediction signals, and canceling the feedback signalfrom the sound signal using the estimated path of the feedback signal.

In another aspect, there is provided a computer-readable storage mediumhaving stored therein program instructions to cause a processor toimplement a method of canceling a feedback signal, the method includingdetermining a linear prediction coefficient that is adaptively adjustedaccording to an input signal of an apparatus for reproducing sound,generating a first prediction signal by linearly predicting the inputsignal using the determined linear prediction coefficient, generating asecond prediction signal by linearly predicting an output signal of theapparatus using the determined linear prediction coefficient, estimatinga path of a feedback signal according to an algorithm for canceling afeedback signal using the first and second prediction signals, andcanceling the feedback signal from the input signal using the estimatedpath of the feedback signal.

Other features and aspects may be apparent from the following detaileddescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an example of an apparatus forreproducing sound.

FIG. 2 is a diagram illustrating another example of the apparatus ofFIG. 1.

FIG. 3 is a diagram illustrating an example of a coefficient determiner.

FIG. 4 is a diagram illustrating an example of a first linear predictor.

FIG. 5 is a diagram illustrating an example of a second linearpredictor.

FIG. 6 is a graph illustrating an example of a variable convergenceconstant having a sigmoid function form.

FIG. 7 is a flowchart illustrating an example a method of reproducingsound.

FIG. 8 is a flowchart illustrating an example of a method of canceling afeedback signal.

Throughout the drawings and the detailed description, unless otherwisedescribed, the same drawing reference numerals will be understood torefer to the same elements, features, and structures. The relative sizeand depiction of these elements may be exaggerated for clarity,illustration, and convenience.

DETAILED DESCRIPTION

The following detailed description is provided to assist the reader ingaining a comprehensive understanding of the methods, apparatuses,and/or systems described herein. Accordingly, various changes,modifications, and equivalents of the methods, apparatuses, and/orsystems described herein will be suggested to those of ordinary skill inthe art. Also, descriptions of well-known functions and constructionsmay be omitted for increased clarity and conciseness.

FIG. 1 illustrates an example of an apparatus for reproducing sound.

Referring to FIG. 1, apparatus 100 includes a sound signal generator110, a signal processor 120, a coefficient determiner 130, a firstlinear predictor 140, a second linear predictor 150, and a feedbacksignal canceler 160.

The apparatus 100 of FIG. 1 shows elements related to the currentexample. Accordingly, it would be obvious to one of ordinary skill inthe art that other general-purpose elements may be further included inthe apparatus 100.

In this example, the signal processor 120, the coefficient determiner130, the first linear predictor 140, the second linear predictor 150,and the feedback signal canceler 160 of the apparatus 100 of FIG. 1 maycorrespond to one or more processors. A processor may include in anarray of a plurality of logic gates, or in a combination of ageneral-purpose microprocessor and a memory storing a program that isexecutable in the general-purpose microprocessor. However, it would beobvious to one of ordinary skill in the art that the processor be inother forms of hardware.

The apparatus 100 may include a hearing aid for amplifying andoutputting peripheral sound. For example, the apparatus 100 may includea device used to reproduce sound in a relay, a telephone, a device forvideo conferencing, a communication device, and the like.

The apparatus 100 may reproduce sound obtained by canceling a feedbacksignal. For example, the feedback signal may be a signal wherein soundoutput from the apparatus 100 is fed back again to the apparatus 100.

For example, the feedback signal may denote a signal that is fed back asvibrations generated by a sound output unit (not shown) of the apparatus100 and that is transmitted to the sound signal generator 110 through ahousing of the apparatus 100.

As another example, if the apparatus 100 is a hearing aid, the feedbacksignal may denote a signal wherein a part of a signal output from thesound output unit of the apparatus 100 is fed back to the sound signalgenerator 110 through a small vent between an outer wall of the hearingaid and an ear of a user.

The apparatus 100 may cancel or otherwise reduce such a feedback signalso that a user of the apparatus 100 hears clearer sound.

The sound signal generator 110 may generate a sound signal by receivingsound around the apparatus 100. For example, the sound signal generator110 may be a microphone that converts received sound to an electricsignal, but is not limited thereto, and may be any device forrecognizing and receiving surrounding sound.

The apparatus 100 may include a plurality of sound signal generators110. Accordingly, if the apparatus 100 is a hearing aid, a sound signalgenerator 110 may be disposed in each of the right and left ears of theuser.

The signal processor 120 may perform a process, according to functionsof the apparatus 100, on a residual signal obtained by canceling thefeedback signal from the sound signal generated by the sound signalgenerator 110. In this example, the residual signal may be an errorsignal. As an example, if the apparatus 100 is a hearing aid, theprocess according to the functions of the apparatus 100 may be a processof converting the sound signal to a digital signal, amplifying thedigital signal, and reconverting the amplified digital signal to thesound signal.

In other words, if the apparatus 100 is used for a relay, the signalprocessor 120 may perform a process according to relaying functions onthe residual signal.

The coefficient determiner 130 may determine a linear predictioncoefficient that is adaptively adjusted according to the residual signalobtained by canceling the feedback signal from the sound signal. Forexample, the coefficient determiner 130 may be a lattice-based linearpredictor, and may determine the linear prediction coefficient accordingto a Burg algorithm by receiving the residual signal.

As an example, the coefficient determiner 130 may determine the linearprediction coefficient so that the residual signal is minimized. Assuch, the coefficient determiner 130 may adaptively determine the linearprediction coefficient using the lattice-based linear predictoraccording to the Burg algorithm. An example of the coefficientdeterminer 130 is described with reference to FIG. 3.

The first linear predictor 140 may output a first prediction signal bylinearly predicting the residual signal according to the linearprediction coefficient determined by the coefficient determiner 130. Forexample, the first linear predictor 140 may be a lattice-based orLevinson-Durbin-based linear predictor, but is not limited thereto.

For example, if the first linear predictor 140 linearly predicts acurrent residual signal using previously input residual signals, alinear prediction coefficient used for the linear prediction may bedetermined by the coefficient determiner 130. Accordingly, the firstlinear predictor 140 may perform a calculation represented by Equation 1below.

ε(n)=a ^(T)(n)e(n)   [Equation 1]

In Equation 1, ε(n) denotes the first prediction signal that ispredicted by the first linear predictor 140, a(n) denotes the linearprediction coefficient that is determined by the coefficient determiner130, and e(n) denotes the residual signal. In other words, the firstlinear predictor 140 may output the first prediction signal ε(n) bylinearly predicting the residual signal e(n) according to the linearprediction coefficient a(n) determined by the coefficient determiner130. As another example, the first linear predictor 140 may predict andoutput the first prediction signal by considering an a priori error.

In Equation 1, n is an integer equal to or greater than 1, and ε(n),e(n), and a(n) are vector signals that are obtained by sampling a signalin terms of time. The description of n and the vector signals may beequally applied to following equations, and thus descriptions thereofwill not be repeated.

An example of the structure of the first linear predictor 140 isdescribed with reference to FIG. 4.

The second linear predictor 150 may output a second prediction signal bylinearly predicting the sound signal that is processed by the signalprocessor 120, according to the linear prediction coefficient determinedby the coefficient determiner 130. Here, the second linear predictor 150may be a lattice-based or Levinson-Durbin-based linear predictor, but isnot limited thereto.

For example, the second linear predictor 150 linear may predict acurrent output signal of the signal processor 120 using previouslyreceived output signals of the signal processor 120, according to thelinear prediction coefficient determined by the coefficient determiner130. Accordingly, the second linear predictor 150 may perform acalculation represented by Equation 2 below.

U(n)=a ^(T)(n)×(n)   [Equation 2]

In Equation 2, U(n) denotes the second prediction signal that ispredicted by the second linear predictor 150, a(n) denotes the linearprediction coefficient that is determined by the coefficient determiner130, and x(n) denotes the sound signal processed by the signal processor120. In other words, the second linear predictor 150 may output thesecond prediction signal U(n) by linearly predicting the sound signalx(n) processed by the signal processor 120, according to the linearprediction coefficient a(n) determined by the coefficient determiner130.

An example of the structure of the second linear predictor 150 isdescribed with reference to FIG. 5.

The feedback signal canceler 160 may estimate a path of the feedbacksignal according to an algorithm for canceling a feedback signal usingthe first prediction signal that is output from the first linearpredictor 140 and the second prediction signal that is output from thesecond linear predictor 150, and may cancel the feedback signal from thesound signal generated by the sound signal generator 110 using theestimated path of the feedback signal.

For example, the algorithm for canceling the feedback signal may be apseudo affine projection (PAP) algorithm or an affine projection (AP)algorithm, but is not limited thereto.

As another example, the algorithm for canceling the feedback signal maybe a normalized least mean square (NLMS) algorithm or a prediction errormethod (PEM) algorithm. Such an algorithm may be applied to the feedbacksignal canceler 160.

For example, the path of the feedback signal estimated according to thePAP algorithm may be represented by Equation 3 below.

$\begin{matrix}{{w( {n + 1} )} = {{w(n)} + {\mu \frac{U(n)}{{{U(n)}}^{2}}{ɛ(n)}}}} & \lbrack {{Equation}\mspace{14mu} 3} \rbrack\end{matrix}$

In Equation 3, w(n+1) denotes the estimated path of the feedback signal,μ denotes a convergence constant, ε(n) denotes the first predictionsignal that is output from the first linear predictor 140, U(n) denotesthe second prediction signal that is output from the second linearpredictor 150, and □·□ denotes a notation indicating a Euclidean vectornorm.

Here, Equation 3 is only an example of the PAP algorithm, and thus thePAP algorithm is not limited thereto.

The PAP algorithm of Equation 3 is the same as a general PAP algorithm,except that the first prediction signal ε(n) that is output from thefirst linear predictor 140 and the second prediction signal U(n) that isoutput from the second linear predictor 150 are used as parameters. Inother words, the PAP algorithm used by the feedback signal canceler 160is the same as the general PAP algorithm except that the parameters ε(n)and U(n) are signals that passed through the first linear predictor 140according to the linear prediction coefficient determined by thecoefficient determiner 130, and the second linear predictor 150according to the linear prediction coefficient determined by thecoefficient determiner 130, respectively. Because the feedbacks signalcanceler 160 estimates the path of the feedback signal using the firstprediction signal that is output from the first linear predictor 140according to the linear prediction coefficient determined by thecoefficient determiner 130, a weight about characteristics of a currentsignal may be largely applied to the estimated path of the feedbacksignal. Accordingly, the feedback signal canceler 160 may stably cancelthe feedback signal.

FIG. 2 illustrates another example of an apparatus for reproducingsound.

Referring to FIG. 2, apparatus 100 includes the sound signal generator110, the signal processor 120, the coefficient determiner 130, the firstlinear predictor 140, the second linear predictor 150, the feedbacksignal canceler 160, and a convergence speed adjustor 170. In thisexample, the feedback signal canceler 160 includes a feedback signalestimator 162, a feedback signal extractor 164, and a residual signalgenerator 166.

The apparatus 100 may further include other general-purpose elements.Also, the signal processor 120, the coefficient determiner 130, thefirst linear predictor 140, the second linear predictor 150, thefeedback signal canceler 160, and the convergence speed adjustor 170 maybe realized in one or more processors.

The apparatus 100 of FIG. 2 is another example of the apparatus 100 ofFIG. 1. Accordingly, the elements of the apparatus 100 are not limitedto those shown in FIG. 2. Also, descriptions related to FIG. 1 areapplicable to the apparatus 100 of FIG. 2, and thus additionalexplanation thereof will not be repeated here.

The sound signal generator 110 may generate the sound signal byreceiving sound around the apparatus 100. The signal processor 120 mayperform the process according to the functions of the apparatus 100 onthe residual signal that is obtained by canceling the feedback signalfrom the sound signal. The coefficient determiner 130 may determine thelinear prediction coefficient that is adaptively adjusted according tothe residual signal. The first linear predictor 140 may output the firstprediction signal by linearly predicting the residual signal accordingto the linear prediction coefficient determined by the coefficientdeterminer 130. The second linear predictor 150 may output the secondprediction signal by linearly predicting the sound signal processed bythe signal processor 120, according to the linear prediction coefficientdetermined by the coefficient determiner 130.

The feedback signal canceler 160 may estimate the path of the feedbacksignal according to the algorithm for canceling a feedback signal usingthe first and second prediction signals. Accordingly, the feedbacksignal canceler 160 may cancel the feedback signal from the sound signalgenerated by the sound signal generator 110 using the estimated path ofthe feedback signal.

The feedback signal estimator 162 may estimate the path of the feedbacksignal using the first prediction signal output from the first linearpredictor 140 and the second prediction signal output from the secondlinear predictor 150. For example, the feedback signal estimator 162 mayestimate the path of the feedback signal according to Equation 3 above,according to the PAP algorithm.

The feedback signal extractor 164 may extract the feedback signal fromthe sound signal that is processed by the signal processor 120 using thepath of the feedback signal that is estimated by the feedback signalestimator 162. For example, the feedback signal extractor 164 may be anadaptive filter. Accordingly, the feedback signal extractor 164 may bean adaptive filter for updating a coefficient according to the path ofthe feedback signal estimated by the feedback signal estimator 162.

The residual signal generator 166 may generate the residual signal bycanceling the feedback signal that is extracted by the feedback signalextractor 164 from the sound signal generated by the sound signalgenerator 110.

Accordingly, the feedback signal canceler 160 may cancel the feedbacksignal of the apparatus 100.

The convergence speed adjustor 170 may adaptively adjust a convergencespeed of the feedback signal extractor 164 according to anautocorrelation of the residual signal. The convergence speed may be astep size.

For example, the convergence speed adjustor 170 may adjust theconvergence speed using a ratio of a first autocorrelation at a firsttime difference to a second autocorrelation at a second time difference.Here, the time difference may be also referred to as a time delay.

For example, the convergence speed adjustor 170 may adjust theconvergence speed using a ratio of an autocorrelation of a residualsignal when a time difference is 0 to an autocorrelation of a residualsignal when a time difference is 1. For example, the autocorrelation ofthe residual signal when the time difference is 0 and theautocorrelation of the residual signal when the time difference is 1 maybe represented by Equations 4 and 5, respectively.

p(n)=λp(n−1)+(1−λ)e ²(n)   [Equation 4]

q(n)=λq(n−1)+(1−λ)e(n)e(n−1)   [Equation 5]

In Equations 4 and 5, p(n) denotes the autocorrelation of the residualsignal when the time difference is 0, e(n) denotes the residual signal,and q(n) denotes the autocorrelation of the residual signal when thetime difference is 1. Also, λ is a value that is obtained viaexperiments, and may be 0.1, but is not limited thereto.

Accordingly, the ratio of the autocorrelation of the residual signalwhen the time difference is 0 to the autocorrelation of the residualsignal when the time difference is 1, which is a correlation factor usedby the convergence speed adjustor 170 to adjust the convergence speed ofthe feedback signal extractor 164, may be represented by Equation 6below.

$\begin{matrix}{{v(n)} = \frac{q^{2}(n)}{p^{2}(n)}} & \lbrack {{Equation}\mspace{14mu} 6} \rbrack\end{matrix}$

In Equation 6, v(n) denotes the correlation factor, p(n) denotes theautocorrelation of the residual signal when the time difference is 0,and q(n) denotes the autocorrelation of the residual signal when thetime difference is 1.

Accordingly, the convergence speed adjustor 170 may adjust theconvergence speed using a sigmoid function according to the ratio of thefirst autocorrelation at the first time difference to the secondautocorrelation at the second time difference. In this example, theconvergence speed adjustor 170 may adjust the convergence speed usingthe sigmoid function according to the correlation factor, which may berepresented by Equation 7 below.

μ(n)=f[1−v(n)]  [Equation 7]

In Equation 7, μ(n) denotes a variable convergence constant, f[·]denotes the sigmoid function, and v(n) denotes the correlation factor.

If a signal having high correlation is input to the apparatus 100, avalue of the correlation factor v(n) may be close to 0, and when asignal having low correlation is input to the apparatus 100, the valueof the correlation factor v(n) may be close to 1. As such, using thecorrelation factor having a linear characteristic from 0 to 1 as theconvergence constant, the convergence speed adjustor 170 may enable theconvergence constant to be small by using the sigmoid function, when acolored signal having high correlation is input.

An example of a variable convergence constant having a sigmoid functionis described with reference to FIG. 6.

The convergence speed adjustor 170 may output the variable convergenceconstant μ(n) to the feedback signal estimator 162, and the feedbacksignal estimator 162 may use the variable convergence constant μ(n) toadaptively adjust the convergence speed of the feedback signal extractor164.

Accordingly, the path of the feedback signal estimated by the feedbacksignal estimator 162 according to the PAP algorithm may be representedby Equation 8 below.

$\begin{matrix}{{w( {n + 1} )} = {{w(n)} + {{\mu (n)}\frac{U(n)}{{{U(n)}}^{2}}{ɛ(n)}}}} & \lbrack {{Equation}\mspace{14mu} 8} \rbrack\end{matrix}$

In Equation 8, w(n+1) denotes the estimated path of the feedback signal,μ(n) denotes the variable convergence constant, ε(n) denotes the firstprediction signal that is output from the first linear predictor 140,U(n) denotes the second prediction signal that is output from the secondlinear predictor 150, and □·□ denotes a notation indicating a Euclideanvector norm.

Equation 8 is the same as Equation 3, except that the variableconvergence constant μ(n) is used, and thus further description thereofwill not be repeated here.

If a signal having a colored characteristic and high correlation isinput to the apparatus 100, the convergence speed adjustor 170 mayoutput a convergence constant that has a low convergence speed to thefeedback signal estimator 162. If a signal having a white noisecharacteristic and low correlation is input to the apparatus 100, theconvergence speed adjustor 170 may output a convergence constant thathas a high convergence speed to the feedback signal estimator 162.Accordingly, the apparatus 100 may quickly estimate the path of thefeedback signal while minimizing distortion of the input sound signal.

If the apparatus 100 is a hearing aid, the PAP algorithm suitable for alow power digital hearing aid may be used while reducing a throughput.Also, the apparatus 100 may stably, quickly, and accurately estimate thepath of the feedback signal while using the variable convergenceconstant, and thus, distortion of an input signal may be minimizedaccording to characteristics of the input signal.

FIG. 3 illustrates an example of a coefficient determiner.

Referring to FIG. 3, a lattice-based m−1th linear predictor 31 is shownas an example of the coefficient determiner 130.

The coefficient determiner 130 according to the current embodiment ofthe may receive the residual signal e(n) to predict an output signalb_(m−1)(n) and may determine linear prediction coefficients K₁ throughK_(m−1)*. In this example, the linear prediction coefficients K₁ throughK_(m−1)* may be also referred to as reflection coefficients. The linearprediction coefficients K₁ through K_(m−1)* may each correspond to a(n)defined in Equations 1 and 2.

The coefficient determiner 130 may adaptively adjust and determine thelinear prediction coefficient using the Burg algorithm according to theresidual signal. For example, the coefficient determiner 130 may receivethe residual signal e(n) and determine the linear predictioncoefficients K₁ through K_(m−1)* such that the residual signal e(n) isminimized according to the Burg algorithm.

The linear prediction coefficients K₁ through K_(m−1)* determined by thecoefficient determiner 130 may be transmitted to the first and secondlinear predictors 140 and 150. The first and second linear predictors140 and 150 may respectively predict the first and second predictionsignals according to the linear prediction coefficients K₁ throughK_(m−1)*.

As such, because the coefficient determiner 130 may adaptively determinethe linear prediction coefficient such that the residual signal e(n) isminimized as the residual signal e(n) changes, accuracy of estimatingthe path of the feedback signal may be improved since characteristics ofa current signal are largely reflected.

FIG. 4 illustrates an example of a first linear predictor.

Referring to FIG. 4, a lattice-based m−1th linear predictor 41 is shownas an example of the first linear predictor 140.

The first linear predictor 140 according to the current embodiment mayreceive the residual signal e(n) to predict the output signal b_(m−1)(n)according to the linear prediction coefficients K₁ through K_(m−1)* thatare determined by the coefficient determiner 130. In this example, thelinear prediction coefficients K₁ through K_(m−1)* determined by thecoefficient determiner 130 may be a(n) of Equation 1, and the outputsignal b_(m−1)(n) may be the first prediction signal ε(n).

In this example, the first linear predictor 140 is the lattice-basedm−1th linear predictor 41 considering an a priori error. Thus, the firstprediction signal output from the lattice-based m−1th linear predictor41 may be represented by Equation 9 below.

ε(n)=a _(μ) ^(T)(n)·e(n)=(Σ_(μ) a(n))^(T) ·e(n)   [Equation 9]

In Equation 9, ε(n) denotes the first prediction signal that ispredicted by the lattice-based m−1th linear predictor 41, a(n) denotesthe linear prediction coefficient that is determined by the coefficientdeterminer 130, and e(n) denotes the residual signal. Also, Σ_(μ) inEquation 9 may be represented by Equation 10 below.

$\begin{matrix}{\Sigma_{\mu} = {{{diag}\{ {( {1 - \mu} ),( {1 - \mu} )^{2},\ldots \mspace{14mu}, ( {1 - \mu} )^{m - 1}} \}} = {\quad\begin{bmatrix}{1 - \mu} & 0 & \ldots & 0 \\0 & ( {1 - \mu} )^{2} & \; & \vdots \\\vdots & \; & \ddots & 0 \\0 & \ldots & 0 & ( {1 - \mu} )^{m - 1}\end{bmatrix}}}} & \lbrack {{Equation}\mspace{14mu} 10} \rbrack\end{matrix}$

In Equation 10, μ denotes a convergence coefficient in an adaptivealgorithm. In this example, μ denotes the convergence constant that isused in Equation 3 or the variable convergence constant that is definedin Equation 7.

Accordingly, the lattice-based m−1th linear predictor 41 considering thea priori error may predict and output the first prediction signal ε(n).

Accordingly, because the first linear predictor 140 linear predicts thefirst prediction signal by considering the a priori error, the apparatus100 may stably cancel the feedback signal.

FIG. 5 illustrates an example of a second linear predictor.

Referring to FIG. 5, a lattice-based m−1th linear predictor 51 is shownas an example of the second linear predictor 150.

The second linear predictor 150 according to the current embodiment mayreceive the sound signal x(n) that passed through the signal processor120, and predict the output signal b_(m−1)(n) according to the linearprediction coefficients K₁ through K_(m−1)* determined by thecoefficient determiner 130. In this example, the linear predictioncoefficients K₁ through K_(m−1)* determined by the coefficientdeterminer 130 may be a(n) of Equation 2, and the output signalb_(m−1)(n) may be the second prediction signal U(n).

FIG. 6 is a graph illustrating a variable convergence constant μ(n)having a sigmoid function form.

Referring to FIGS. 2 and 6, as defined in Equation 7, the convergencespeed adjustor 170 may adjust the variable convergence constant μ(n)using the sigmoid function according to the ratio v(n) of the firstautocorrelation at the first time difference to the secondautocorrelation at the second time difference. Accordingly, theconvergence speed adjustor 170 may adaptively adjust the convergencespeed of the feedback signal extractor 164.

If a signal having a colored characteristic and high correlation isinput to the apparatus 100, the convergence speed adjustor 170 mayoutput a convergence constant that has a low convergence speed to thefeedback signal estimator 162, and if a signal having a white noisecharacteristic and low correlation is input to the apparatus 100, theconvergence speed adjustor 170 may output a convergence constant thathas a high convergence speed to the feedback signal estimator 162.Accordingly, the apparatus 100 may quickly estimate the path of thefeedback signal while minimizing distortion of the input sound signal.

FIG. 7 illustrates an example of a method of reproducing sound.

Referring to FIG. 7, the method includes operations that may beprocessed in time series by the apparatus 100. Accordingly, the examplesdescribed with reference to the apparatus 100 may be equally applied tothe method of FIG. 7, even if they are not described.

In 701, the sound signal generator 110 generates the sound signal byreceiving the sound around the apparatus 100. For example, 701 may beperformed using a microphone that is included in the apparatus 100.

In 702, the signal processor 120 generates the output signal byperforming a process according to the functions of the apparatus 100, onthe residual signal that is obtained by canceling the feedback signalfrom the sound signal generated in 701. Here, the generated outputsignal may be the sound signal on which the signal processing isperformed by the signal processor 120.

In 703, the coefficient determiner 130 determines the linear predictioncoefficient that is adaptively adjusted according to the residualsignal. For example, 703 may be performed using a lattice-based linearpredictor according to a Burg algorithm.

In 704, the first linear predictor 140 generates the first predictionsignal by linearly predicting the residual signal according to thelinear prediction coefficient determined in 703. For example, 704 may beperformed by a lattice-based or Levinson-Durbin-based linear predictor.

In 705, the second linear predictor 150 generates the second predictionsignal by linearly predicting the output signal generated in 702according to the linear prediction coefficient determined in 703. Forexample, 705 may be performed by a lattice-based orLevinson-Durbin-based linear predictor.

In 706, the feedback signal estimator 162 estimates the path of thefeedback signal according to the algorithm for canceling a feedbacksignal using the first prediction signal generated in 704 and the secondprediction signal generated in 705. For example, the algorithm forcanceling the feedback signal may be a PAP algorithm.

In 707, the feedback signal extractor 164 and the residual signalgenerator 166 cancel the feedback signal from the sound signal using thepath of the feedback signal estimated in 706. For example, 707 may beperformed by extracting the feedback signal using an adaptive filter andgenerating the residual signal by canceling the extracted feedbacksignal from the output signal.

Accordingly, the apparatus 100 may generate sound in which the feedbacksignal is stably and cleanly canceled.

FIG. 8 illustrates an example of a method of canceling a feedbacksignal. The method may be used to cancel a feedback signal in theapparatus 100 of FIG. 1 or 2.

Referring to FIG. 8, the method may include operations processed in timeseries by the apparatus 100. Accordingly, the examples described withreference to the apparatus 100 may be equally applied to the method ofFIG. 8, even if they are not described.

In 801, the coefficient determiner 130 determines the linear predictioncoefficient that is adaptively adjusted according to the input signal ofthe apparatus 100. For example, the input signal of the apparatus 100may include any signal that is input to the apparatus 100. Also, 801 maybe performed using a lattice-based linear predictor according to a Burgalgorithm.

In 802, the first linear predictor 140 generates the first predictionsignal by linearly predicting the input signal of the apparatus 100,according to the linear prediction coefficient determined in 801. Forexample, the first linear predictor 140 may be disposed at an inputterminal of the apparatus 100. For example, 802 may be performed using alattice-based or Levinson-Durbin-based linear predictor.

In 803, the second linear predictor 150 generates the second predictionsignal by linearly predicting an output signal of the apparatus 100,according to the linear prediction coefficient determined in 801. Forexample, the second linear predictor 150 may be disposed at an outputterminal of the apparatus 100. For example, 803 may be performed by alattice-based or Levinson-Durbin-based linear predictor.

In 804, the feedback signal estimator 162 estimates the path of thefeedback signal according to an algorithm for canceling a feedbacksignal, using the first prediction signal generated in 802 and thesecond prediction signal generated in 803. For example, the algorithmfor canceling the feedback signal may be a PAP algorithm.

In 805, the feedback signal extractor 164 and the residual signalgenerator 166 cancels the feedback signal from the input signal of theapparatus 100 using the path of the feedback signal estimated in 804.For example, 805 may be performed by extracting the feedback signalusing an adaptive filter, and generating the residual signal bycanceling the extracted feedback signal from the output signal.

Accordingly, the apparatus 100 may stably and cleanly cancel thefeedback signal.

In various examples the apparatus 100 has a simple structure, has stableconvergence performance, and quickly and accurately estimates the pathof the feedback signal. Accordingly, the apparatus 100 may minimizesound distortion in the input signal, and stably cancel the feedbacksignal.

As described herein, according to one or more embodiments, a feedbacksignal can be stably cancelled without distortion of an input signalusing a plurality of linear predictors.

Program instructions to perform a method described herein, or one ormore operations thereof, may be recorded, stored, or fixed in one ormore computer-readable storage media. The program instructions may beimplemented by a computer. For example, the computer may cause aprocessor to execute the program instructions. The media may include,alone or in combination with the program instructions, data files, datastructures, and the like. Examples of computer-readable storage mediainclude magnetic media, such as hard disks, floppy disks, and magnetictape; optical media such as CD ROM disks and DVDs; magneto-opticalmedia, such as optical disks; and hardware devices that are speciallyconfigured to store and perform program instructions, such as read-onlymemory (ROM), random access memory (RAM), flash memory, and the like.Examples of program instructions include machine code, such as producedby a compiler, and files containing higher level code that may beexecuted by the computer using an interpreter. The program instructions,that is, software, may be distributed over network coupled computersystems so that the software is stored and executed in a distributedfashion. For example, the software and data may be stored by one or morecomputer readable storage mediums. Also, functional programs, codes, andcode segments for accomplishing the example embodiments disclosed hereincan be easily construed by programmers skilled in the art to which theembodiments pertain based on and using the flow diagrams and blockdiagrams of the figures and their corresponding descriptions as providedherein. Also, the described unit to perform an operation or a method maybe hardware, software, or some combination of hardware and software. Forexample, the unit may be a software package running on a computer or thecomputer on which that software is running.

A number of examples have been described above. Nevertheless, it will beunderstood that various modifications may be made. For example, suitableresults may be achieved if the described techniques are performed in adifferent order and/or if components in a described system,architecture, device, or circuit are combined in a different mannerand/or replaced or supplemented by other components or theirequivalents. Accordingly, other implementations are within the scope ofthe following claims.

1. An apparatus for reproducing sound, the apparatus comprising: a soundsignal generator configured to generate a sound signal by receivingsound from around the apparatus; a signal processor configured toperform a process according to functions of the apparatus, on a residualsignal that is obtained by canceling a feedback signal from the soundsignal; a coefficient determiner configured to determine a linearprediction coefficient that is adaptively adjusted according to theresidual signal; a first linear predictor configured to output a firstprediction signal by linearly predicting the residual signal accordingto the determined linear prediction coefficient; a second linearpredictor configured to output a second prediction signal by linearlypredicting the sound signal that is processed by the signal processor,according to the determined linear prediction coefficient; and afeedback signal canceler configured to estimate a path of the feedbacksignal according to an algorithm for canceling a feedback signal usingthe first and second prediction signals, and to cancel the feedbacksignal from the sound signal generated by the sound signal generatorusing the estimated path of the feedback signal.
 2. The apparatus ofclaim 1, wherein the algorithm for canceling the feedback signalcomprises a pseudo affine projection (PAP) algorithm.
 3. The apparatusof claim 1, wherein the feedback signal canceler comprises: a feedbacksignal estimator configured to estimate the path of the feedback signalusing the first and second prediction signals; a feedback signalextractor configured to extract the feedback signal from the soundsignal that is processed by the signal processor, using the estimatedpath of the feedback signal; and a residual signal generator configuredto generate the residual signal by canceling the extracted feedbacksignal from the sound signal generated by the sound signal generator. 4.The apparatus of claim 3, wherein the feedback signal extractor is anadaptive filter that updates a coefficient according to the path of thefeedback signal estimated by the feedback signal estimator.
 5. Theapparatus of claim 1, wherein at least one of the coefficientdeterminer, the first linear predictor, and the second linear predictorcomprises a lattice-based linear predictor.
 6. The apparatus of claim 5,wherein the first linear predictor is configured to output the firstprediction signal by considering an a priori error.
 7. The apparatus ofclaim 5, wherein the coefficient determiner is configured to determinethe linear prediction coefficient according to a Burg algorithm.
 8. Theapparatus of claim 3, further comprising a convergence speed adjustorconfigured to adaptively adjust a convergence speed of the feedbacksignal extractor according to an autocorrelation of the residual signal.9. The apparatus of claim 8, wherein the convergence speed adjustor isconfigured to adjust a convergence speed using a ratio of a firstautocorrelation at a first time difference to a second autocorrelationat a second time difference.
 10. The apparatus of claim 9, wherein theconvergence speed adjustor is configured to adjust a convergence speedusing a sigmoid function according to the ratio of the firstautocorrelation at the first time difference to the secondautocorrelation at the second time difference.
 11. A method ofreproducing sound in an apparatus for reproducing sound, the methodcomprising: generating a sound signal by receiving sound from around theapparatus; generating an output signal by performing a process accordingto functions of the apparatus, on a residual signal that is obtained bycanceling a feedback signal from the sound signal; determining a linearprediction coefficient that is adaptively adjusted according to theresidual signal; generating a first prediction signal by linearlypredicting the residual signal according to the determined linearprediction coefficient; generating a second prediction signal bylinearly predicting the output signal according to the determined linearprediction coefficient; estimating a path of the feedback signalaccording to an algorithm for canceling a feedback signal using thefirst and second prediction signals; and canceling the feedback signalfrom the sound signal using the estimated path of the feedback signal.12. The method of claim 11, wherein the algorithm for canceling thefeedback signal comprises a pseudo affine projection (PAP) algorithm.13. The method of claim 11, wherein the canceling of the feedback signalcomprises: extracting the feedback signal from the output signal usingthe estimated path of the feedback signal; and generating the residualsignal by canceling the extracted feedback signal from the sound signal.14. The method of claim 11, wherein at least one of the determining ofthe linear prediction coefficient, the generating of the firstprediction signal, and the generating of the first prediction signal isperformed using a lattice-based linear predictor.
 15. The method ofclaim 14, wherein the generating of the first prediction signal isperformed using a lattice-based linear predictor by considering an apriori error.
 16. The method of claim 14, wherein the determining of thelinear prediction coefficient is performed according to a Burgalgorithm.
 17. The method of claim 13, wherein the extracting of thefeedback signal is performed using an adaptive filter, and the methodfurther comprises adaptively adjusting a convergence speed of theadaptive filter according to an autocorrelation of the residual signal.18. A method of canceling a feedback signal, the method comprising:determining a linear prediction coefficient that is adaptively adjustedaccording to an input signal of an apparatus for reproducing sound;generating a first prediction signal by linearly predicting the inputsignal using the determined linear prediction coefficient; generating asecond prediction signal by linearly predicting an output signal of theapparatus using the determined linear prediction coefficient; estimatinga path of a feedback signal according to an algorithm for canceling afeedback signal using the first and second prediction signals; andcanceling the feedback signal from the input signal using the estimatedpath of the feedback signal.
 19. A computer-readable storage mediumhaving stored therein program instructions to cause a processor toimplement a method of reproducing sound in an apparatus for reproducingsound, the method comprising: generating a sound signal by receivingsound from around the apparatus; generating an output signal byperforming a process according to functions of the apparatus, on aresidual signal that is obtained by canceling a feedback signal from thesound signal; determining a linear prediction coefficient that isadaptively adjusted according to the residual signal; generating a firstprediction signal by linearly predicting the residual signal accordingto the determined linear prediction coefficient; generating a secondprediction signal by linearly predicting the output signal according tothe determined linear prediction coefficient; estimating a path of thefeedback signal according to an algorithm for canceling a feedbacksignal using the first and second prediction signals; and canceling thefeedback signal from the sound signal using the estimated path of thefeedback signal.
 20. A computer-readable storage medium having storedtherein program instructions to cause a processor to implement a methodof canceling a feedback signal, the method comprising: determining alinear prediction coefficient that is adaptively adjusted according toan input signal of an apparatus for reproducing sound; generating afirst prediction signal by linearly predicting the input signal usingthe determined linear prediction coefficient; generating a secondprediction signal by linearly predicting an output signal of theapparatus using the determined linear prediction coefficient; estimatinga path of a feedback signal according to an algorithm for canceling afeedback signal using the first and second prediction signals; andcanceling the feedback signal from the input signal using the estimatedpath of the feedback signal.